Gamma/Hadron Separation with the HAWC Observatory

Abstract

The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority ($>99.9\%$) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.

35 pages, 9 figures, published in Nuclear Instruments and Methods in Physics Research Section A

References in corpus (6)

• Classification without labels: Learning from mixed samples in high energy physics
• TeV Gamma-Ray Sources from a Survey of the Galactic Plane with Milagro
• Implementation of the Random Forest Method for the Imaging Atmospheric Cherenkov Telescope MAGIC
• Gamma-Hadron Separation in Very-High-Energy gamma-ray astronomy using a multivariate analysis method
• Gamma-Hadron Separation Methods for the VERITAS Array of Four Imaging Atmospheric Cherenkov Telescopes
• Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees

Cited by in corpus (3)

• Discriminating sub-TeV gamma and hadron-induced showers through their footprints
• How to detect gamma-rays from ground: an introduction to the detection concepts
• Application of Graph Networks to a wide-field Water-Cherenkov-based Gamma-Ray Observatory